{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the Data\n",
    "Exploration of all elements in Quarterly Reports and their likelihood to influence future price movement which indicates trade decision in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pandas import Series,DataFrame \n",
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get final data generated by preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = pd.read_excel('final_data.xlsx')\n",
    "final_df = final_df.drop(final_df.columns[0], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the count of Buy, Hold, and Sell\n",
    "Visual check for any class imbalance among the quarterly reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating each class into respective DataFrames\n",
    "sell_df = final_df[final_df['Decision']==0].loc[:, final_df.columns != 'Decision'].reset_index(drop=True)\n",
    "buy_df = final_df[final_df['Decision']==1].loc[:, final_df.columns != 'Decision'].reset_index(drop=True)\n",
    "hold_df = final_df[final_df['Decision']==2].loc[:, final_df.columns != 'Decision'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gross revenue</th>\n",
       "      <th>revenue</th>\n",
       "      <th>total operating cost</th>\n",
       "      <th>selling expense</th>\n",
       "      <th>administration expense</th>\n",
       "      <th>financial expense</th>\n",
       "      <th>net investment income</th>\n",
       "      <th>operating profit</th>\n",
       "      <th>retained profit</th>\n",
       "      <th>net income attributable to parent company</th>\n",
       "      <th>basic EPS</th>\n",
       "      <th>total share (10,000 shares)</th>\n",
       "      <th>total assets</th>\n",
       "      <th>current assets</th>\n",
       "      <th>total liabilities</th>\n",
       "      <th>current liabilities</th>\n",
       "      <th>minority equity</th>\n",
       "      <th>intangible assets</th>\n",
       "      <th>goodwill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.547810</td>\n",
       "      <td>20.547810</td>\n",
       "      <td>21.917360</td>\n",
       "      <td>40.493611</td>\n",
       "      <td>32.815526</td>\n",
       "      <td>-25.273962</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>17.289399</td>\n",
       "      <td>22.804241</td>\n",
       "      <td>22.507598</td>\n",
       "      <td>22.480620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.772301</td>\n",
       "      <td>-8.923411</td>\n",
       "      <td>-26.541283</td>\n",
       "      <td>-15.713399</td>\n",
       "      <td>-8.425051</td>\n",
       "      <td>4.084908</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.996168</td>\n",
       "      <td>12.996168</td>\n",
       "      <td>2.929985</td>\n",
       "      <td>-20.751992</td>\n",
       "      <td>9.729352</td>\n",
       "      <td>-29.727009</td>\n",
       "      <td>-57.911291</td>\n",
       "      <td>60.846788</td>\n",
       "      <td>59.022115</td>\n",
       "      <td>58.765778</td>\n",
       "      <td>58.837209</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.888365</td>\n",
       "      <td>14.419261</td>\n",
       "      <td>-1.776355</td>\n",
       "      <td>-2.416558</td>\n",
       "      <td>2.894519</td>\n",
       "      <td>-0.910106</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.580869</td>\n",
       "      <td>7.580869</td>\n",
       "      <td>-1.592748</td>\n",
       "      <td>-35.788508</td>\n",
       "      <td>18.578732</td>\n",
       "      <td>-103.484603</td>\n",
       "      <td>57.560683</td>\n",
       "      <td>23.908331</td>\n",
       "      <td>28.435625</td>\n",
       "      <td>28.426094</td>\n",
       "      <td>19.896641</td>\n",
       "      <td>7.108014</td>\n",
       "      <td>18.681648</td>\n",
       "      <td>49.476271</td>\n",
       "      <td>4.003814</td>\n",
       "      <td>7.674436</td>\n",
       "      <td>3.959611</td>\n",
       "      <td>-0.926466</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.356531</td>\n",
       "      <td>14.356531</td>\n",
       "      <td>27.121710</td>\n",
       "      <td>0.349871</td>\n",
       "      <td>-20.667307</td>\n",
       "      <td>-8548.874321</td>\n",
       "      <td>63.586382</td>\n",
       "      <td>-3.328906</td>\n",
       "      <td>-7.271905</td>\n",
       "      <td>-7.249617</td>\n",
       "      <td>-7.255747</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.101489</td>\n",
       "      <td>-4.177791</td>\n",
       "      <td>-14.450962</td>\n",
       "      <td>-9.503819</td>\n",
       "      <td>641.491151</td>\n",
       "      <td>7.881599</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-5.870604</td>\n",
       "      <td>-5.870604</td>\n",
       "      <td>-6.495463</td>\n",
       "      <td>-16.254411</td>\n",
       "      <td>159.101641</td>\n",
       "      <td>-101.715141</td>\n",
       "      <td>-97.064021</td>\n",
       "      <td>-5.273053</td>\n",
       "      <td>-0.108514</td>\n",
       "      <td>-0.133745</td>\n",
       "      <td>-0.116189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.546865</td>\n",
       "      <td>19.078899</td>\n",
       "      <td>17.850227</td>\n",
       "      <td>19.197520</td>\n",
       "      <td>0.510986</td>\n",
       "      <td>-0.983750</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gross revenue    revenue  total operating cost  selling expense  \\\n",
       "0      20.547810  20.547810             21.917360        40.493611   \n",
       "1      12.996168  12.996168              2.929985       -20.751992   \n",
       "2       7.580869   7.580869             -1.592748       -35.788508   \n",
       "3      14.356531  14.356531             27.121710         0.349871   \n",
       "4      -5.870604  -5.870604             -6.495463       -16.254411   \n",
       "\n",
       "   administration expense  financial expense  net investment income   \\\n",
       "0               32.815526         -25.273962             -100.000000   \n",
       "1                9.729352         -29.727009              -57.911291   \n",
       "2               18.578732        -103.484603               57.560683   \n",
       "3              -20.667307       -8548.874321               63.586382   \n",
       "4              159.101641        -101.715141              -97.064021   \n",
       "\n",
       "   operating profit  retained profit  \\\n",
       "0         17.289399        22.804241   \n",
       "1         60.846788        59.022115   \n",
       "2         23.908331        28.435625   \n",
       "3         -3.328906        -7.271905   \n",
       "4         -5.273053        -0.108514   \n",
       "\n",
       "   net income attributable to parent company  basic EPS  \\\n",
       "0                                  22.507598  22.480620   \n",
       "1                                  58.765778  58.837209   \n",
       "2                                  28.426094  19.896641   \n",
       "3                                  -7.249617  -7.255747   \n",
       "4                                  -0.133745  -0.116189   \n",
       "\n",
       "   total share (10,000 shares)  total assets  current assets  \\\n",
       "0                     0.000000     -3.772301       -8.923411   \n",
       "1                     0.000000      2.888365       14.419261   \n",
       "2                     7.108014     18.681648       49.476271   \n",
       "3                     0.000000      2.101489       -4.177791   \n",
       "4                     0.000000      7.546865       19.078899   \n",
       "\n",
       "   total liabilities  current liabilities  minority equity  intangible assets  \\\n",
       "0         -26.541283           -15.713399        -8.425051           4.084908   \n",
       "1          -1.776355            -2.416558         2.894519          -0.910106   \n",
       "2           4.003814             7.674436         3.959611          -0.926466   \n",
       "3         -14.450962            -9.503819       641.491151           7.881599   \n",
       "4          17.850227            19.197520         0.510986          -0.983750   \n",
       "\n",
       "   goodwill  \n",
       "0       0.0  \n",
       "1       0.0  \n",
       "2       0.0  \n",
       "3       0.0  \n",
       "4       0.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sell_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the count of each DataFrame of each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "plt.bar(\"Buy\", buy_df.shape[0])\n",
    "plt.bar(\"Sell\", sell_df.shape[0])\n",
    "plt.bar(\"Hold\", hold_df.shape[0])\n",
    "\n",
    "plt.ylabel(\"Number of Quarterly Reports\")\n",
    "plt.title('Count of Quarterly Reports')\n",
    "plt.savefig(\"class_count.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations\n",
    "Checking for any correlation between the future price and the current quarter's features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x72 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation DF of all classes\n",
    "corr = final_df.corr().iloc[[-1],0:-1]\n",
    "# Plot the Correlation DF as a heatmap\n",
    "plt.figure(figsize=(16,1))\n",
    "sns.heatmap(corr, annot=True, linewidths=.1, cbar=True, cmap=\"coolwarm\")\n",
    "plt.xticks()\n",
    "plt.yticks(rotation=0)\n",
    "plt.savefig(\"correlation_feature_price.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "Reduce the feature set for computational simplicity and potential improvement in accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importances from the DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "260 ms ± 41.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# n_estimators: 弱学习器的最大迭代次数，或者说最大的弱学习器的个数。\n",
    "#               一般来说n_estimators太小，容易欠拟合，n_estimators太大，计算量会太大.\n",
    "#               并且n_estimators到一定的数量后，再增大n_estimators获得的模型提升会很小，所以一般选择一个适中的数值。\n",
    "forest = ExtraTreesClassifier(n_estimators=200)\n",
    "\n",
    "# Setting the corresponding variables for our classifier\n",
    "X = final_df.drop(['Decision'], 1)\n",
    "y = final_df.Decision\n",
    "\n",
    "# Fitting the classifier\n",
    "%timeit forest.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6 16  5  4 17  8  9 10  0 12 13 18  2 14  3  1 15  7 11]\n"
     ]
    }
   ],
   "source": [
    "# Determining the important features\n",
    "importances = forest.feature_importances_\n",
    "# The standard deviation among the trees for the important features\n",
    "std = np.std([i.feature_importances_ for i in forest.estimators_], axis=0)\n",
    "# Indexing and sorting the important features\n",
    "indices = np.argsort(importances)[::-1]\n",
    "print(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Rankings:\n",
      "1. net investment income : 0.06980129051317549\n",
      "2. minority equity: 0.06908877131696751\n",
      "3. financial expense: 0.0676569899515931\n",
      "4. administration expense: 0.06531615096019552\n",
      "5. intangible assets: 0.06165471745768966\n",
      "6. retained profit: 0.05774419467159958\n",
      "7. net income attributable to parent company: 0.05571037374660925\n",
      "8. basic EPS: 0.055334490772252304\n",
      "9. gross revenue: 0.05426819668863085\n",
      "10. total assets: 0.052108937598502644\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "print(\"Feature Rankings:\")\n",
    "\n",
    "# Showing the top 10 features\n",
    "for i in range(10):\n",
    "    # 字符串前面加f表示格式化字符串，省去str相加\n",
    "    print(f\"{i+1}. {X.columns[indices[i]]}: {importances[indices[i]]}\")\n",
    "    \n",
    "# Plotting the top 10 features\n",
    "plt.figure(figsize=(14,7))\n",
    "\n",
    "plt.title(\"Feature Importances for the Original Dataset\")\n",
    "plt.bar(range(X.shape[1]), importances[indices], yerr=std[indices], align='center')\n",
    "\n",
    "plt.xticks(range(X.shape[1]), X.columns[indices], rotation=90)\n",
    "plt.xlim([-.5, 9.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select the Top 10 most important features\n",
    "According to the feature importances from the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>net investment income</th>\n",
       "      <th>minority equity</th>\n",
       "      <th>financial expense</th>\n",
       "      <th>administration expense</th>\n",
       "      <th>intangible assets</th>\n",
       "      <th>retained profit</th>\n",
       "      <th>net income attributable to parent company</th>\n",
       "      <th>basic EPS</th>\n",
       "      <th>gross revenue</th>\n",
       "      <th>total assets</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-11.531508</td>\n",
       "      <td>217.567873</td>\n",
       "      <td>-34.057217</td>\n",
       "      <td>-0.765487</td>\n",
       "      <td>61.260408</td>\n",
       "      <td>61.488611</td>\n",
       "      <td>61.538462</td>\n",
       "      <td>-4.945285</td>\n",
       "      <td>2.636775</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-8.425051</td>\n",
       "      <td>-25.273962</td>\n",
       "      <td>32.815526</td>\n",
       "      <td>4.084908</td>\n",
       "      <td>22.804241</td>\n",
       "      <td>22.507598</td>\n",
       "      <td>22.480620</td>\n",
       "      <td>20.547810</td>\n",
       "      <td>-3.772301</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>305.734905</td>\n",
       "      <td>0.589284</td>\n",
       "      <td>-42.209071</td>\n",
       "      <td>-27.876969</td>\n",
       "      <td>-0.892785</td>\n",
       "      <td>-11.492735</td>\n",
       "      <td>-11.276715</td>\n",
       "      <td>-11.211573</td>\n",
       "      <td>-10.876368</td>\n",
       "      <td>1.331521</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-50.975832</td>\n",
       "      <td>-7.052970</td>\n",
       "      <td>-9.402573</td>\n",
       "      <td>39.041926</td>\n",
       "      <td>-0.903730</td>\n",
       "      <td>-12.511143</td>\n",
       "      <td>-12.386752</td>\n",
       "      <td>-12.423625</td>\n",
       "      <td>-3.551234</td>\n",
       "      <td>7.269940</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-57.911291</td>\n",
       "      <td>2.894519</td>\n",
       "      <td>-29.727009</td>\n",
       "      <td>9.729352</td>\n",
       "      <td>-0.910106</td>\n",
       "      <td>59.022115</td>\n",
       "      <td>58.765778</td>\n",
       "      <td>58.837209</td>\n",
       "      <td>12.996168</td>\n",
       "      <td>2.888365</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   net investment income   minority equity  financial expense  \\\n",
       "0             -100.000000       -11.531508         217.567873   \n",
       "1             -100.000000        -8.425051         -25.273962   \n",
       "2              305.734905         0.589284         -42.209071   \n",
       "3              -50.975832        -7.052970          -9.402573   \n",
       "4              -57.911291         2.894519         -29.727009   \n",
       "\n",
       "   administration expense  intangible assets  retained profit  \\\n",
       "0              -34.057217          -0.765487        61.260408   \n",
       "1               32.815526           4.084908        22.804241   \n",
       "2              -27.876969          -0.892785       -11.492735   \n",
       "3               39.041926          -0.903730       -12.511143   \n",
       "4                9.729352          -0.910106        59.022115   \n",
       "\n",
       "   net income attributable to parent company  basic EPS  gross revenue  \\\n",
       "0                                  61.488611  61.538462      -4.945285   \n",
       "1                                  22.507598  22.480620      20.547810   \n",
       "2                                 -11.276715 -11.211573     -10.876368   \n",
       "3                                 -12.386752 -12.423625      -3.551234   \n",
       "4                                  58.765778  58.837209      12.996168   \n",
       "\n",
       "   total assets  Decision  \n",
       "0      2.636775         2  \n",
       "1     -3.772301         0  \n",
       "2      1.331521         1  \n",
       "3      7.269940         1  \n",
       "4      2.888365         0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign the top 10 features as a new DF\n",
    "column_names = list(X.columns[indices])[:10]\n",
    "column_names.append('Decision')\n",
    "top10_df = final_df[column_names]\n",
    "top10_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the Top 10 important feature Dataframe\n",
    "In regard to importance of feature on ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "top10_df.to_excel('top10_features.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importances from the Correlation DF\n",
    "Determining the top 10 most important features based on their correlation value with decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>net investment income</th>\n",
       "      <td>0.181811</td>\n",
       "    </tr>\n",
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       "      <th>financial expense</th>\n",
       "      <td>0.181079</td>\n",
       "    </tr>\n",
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       "      <th>total share (10,000 shares)</th>\n",
       "      <td>0.145458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>administration expense</th>\n",
       "      <td>0.136855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intangible assets</th>\n",
       "      <td>0.130840</td>\n",
       "    </tr>\n",
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       "      <th>goodwill</th>\n",
       "      <td>0.128121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>0.108139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gross revenue</th>\n",
       "      <td>0.108139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic EPS</th>\n",
       "      <td>0.095360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minority equity</th>\n",
       "      <td>0.091243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Decision\n",
       "net investment income        0.181811\n",
       "financial expense            0.181079\n",
       "total share (10,000 shares)  0.145458\n",
       "administration expense       0.136855\n",
       "intangible assets            0.130840\n",
       "goodwill                     0.128121\n",
       "revenue                      0.108139\n",
       "gross revenue                0.108139\n",
       "basic EPS                    0.095360\n",
       "minority equity              0.091243"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_corr = corr.T.apply(abs).sort_values(by='Decision', ascending=False)[:10]\n",
    "top10_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>net investment income</th>\n",
       "      <th>financial expense</th>\n",
       "      <th>total share (10,000 shares)</th>\n",
       "      <th>administration expense</th>\n",
       "      <th>intangible assets</th>\n",
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       "      <th>revenue</th>\n",
       "      <th>gross revenue</th>\n",
       "      <th>basic EPS</th>\n",
       "      <th>minority equity</th>\n",
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       "      <th>0</th>\n",
       "      <td>-100.000000</td>\n",
       "      <td>217.567873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-34.057217</td>\n",
       "      <td>-0.765487</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.945285</td>\n",
       "      <td>-4.945285</td>\n",
       "      <td>61.538462</td>\n",
       "      <td>-11.531508</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-100.000000</td>\n",
       "      <td>-25.273962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.815526</td>\n",
       "      <td>4.084908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.547810</td>\n",
       "      <td>20.547810</td>\n",
       "      <td>22.480620</td>\n",
       "      <td>-8.425051</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>305.734905</td>\n",
       "      <td>-42.209071</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-27.876969</td>\n",
       "      <td>-0.892785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.876368</td>\n",
       "      <td>-10.876368</td>\n",
       "      <td>-11.211573</td>\n",
       "      <td>0.589284</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-50.975832</td>\n",
       "      <td>-9.402573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.041926</td>\n",
       "      <td>-0.903730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.551234</td>\n",
       "      <td>-3.551234</td>\n",
       "      <td>-12.423625</td>\n",
       "      <td>-7.052970</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-57.911291</td>\n",
       "      <td>-29.727009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.729352</td>\n",
       "      <td>-0.910106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.996168</td>\n",
       "      <td>12.996168</td>\n",
       "      <td>58.837209</td>\n",
       "      <td>2.894519</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   net investment income   financial expense  total share (10,000 shares)  \\\n",
       "0             -100.000000         217.567873                          0.0   \n",
       "1             -100.000000         -25.273962                          0.0   \n",
       "2              305.734905         -42.209071                          0.0   \n",
       "3              -50.975832          -9.402573                          0.0   \n",
       "4              -57.911291         -29.727009                          0.0   \n",
       "\n",
       "   administration expense  intangible assets  goodwill    revenue  \\\n",
       "0              -34.057217          -0.765487       0.0  -4.945285   \n",
       "1               32.815526           4.084908       0.0  20.547810   \n",
       "2              -27.876969          -0.892785       0.0 -10.876368   \n",
       "3               39.041926          -0.903730       0.0  -3.551234   \n",
       "4                9.729352          -0.910106       0.0  12.996168   \n",
       "\n",
       "   gross revenue  basic EPS  minority equity  Decision  \n",
       "0      -4.945285  61.538462       -11.531508         2  \n",
       "1      20.547810  22.480620        -8.425051         0  \n",
       "2     -10.876368 -11.211573         0.589284         1  \n",
       "3      -3.551234 -12.423625        -7.052970         1  \n",
       "4      12.996168  58.837209         2.894519         0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_corr_df = final_df[top10_corr.index].join(final_df.Decision)\n",
    "top10_corr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the DF of Top 10 Correlated Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "top10_corr_df.to_excel('top10_corr_features.xlsx')"
   ]
  }
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